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Dynamic segmentation with growth mixture models

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  • Francesca Bassi

    (University of Padua)

Abstract

This paper proposes a new approach to dynamically segment markets. Dynamic segmentation i of key importance in many markets where it is unrealistic to assume stationary segments due to the dynamics in consumers’ needs and product choices. The main goal of the study is to analyse the dynamic process of financial product ownership under the assumption of heterogeneous growth in different segments taking into account significant determinants of growth trajectories. Using data from 2002 to 2010 collected by the Survey of Household Income and Wealth conducted by the Bank of Italy, this article shows that the Italian market of financial products is segmented and that this behavior’s trajectories over time are significantly influenced by the area of the country where the family lives and head of household’s education and gender.

Suggested Citation

  • Francesca Bassi, 2016. "Dynamic segmentation with growth mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 263-279, June.
    • Handle: RePEc:spr:advdac:v:10:y:2016:i:2:d:10.1007_s11634-015-0230-x
    DOI: 10.1007/s11634-015-0230-x
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    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Francesca Bassi, 2013. "Analysing markets within the latent class approach: an application to the pharma sector," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(3), pages 199-207, May.
    3. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    4. Green, Paul E & Carmone, Frank J & Wachspress, David P, 1976. "Consumer Segmentation via Latent Class Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 3(3), pages 170-174, December.
    5. M. Salgueiro & Peter Smith & Marcel Vieira, 2013. "A multi-process second-order latent growth curve model for subjective well-being," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 735-752, February.
    6. Konuş, Umut & Verhoef, Peter C. & Neslin, Scott A., 2008. "Multichannel Shopper Segments and Their Covariates," Journal of Retailing, Elsevier, vol. 84(4), pages 398-413.
    7. Francesca Bassi, 2007. "Latent class factor models for market segmentation: an application to pharmaceuticals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(2), pages 279-287, August.
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    Cited by:

    1. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Lucas Lopes Ferreira de Souza & Francesca Bassi & Ana Augusta Ferreira de Freitas, 2021. "Longitudinal analysis of microfinance borrowers in Brazil: A dynamic market segmentation," Journal of International Development, John Wiley & Sons, Ltd., vol. 33(6), pages 1063-1083, August.

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